Performance of GME

Loading Libraries and Getting ticker info

library(plotly)
library(quantmod)
library(TTR)
library(tidyverse)

getSymbols("GME", source = 'yahoo')

[1] "GME"

Cleaning Data and Generating Signals

df <- data.frame(Date = index(GME), coredata(GME))

df <- df %>%
        mutate(movement = if_else(GME.Close >= GME.Open, "Increasing", "Decreasing")) %>%
        mutate(avg_30 = SMA(GME.Close, n = 30),
               avg_90 = SMA(GME.Close, n = 90)) %>%
        mutate(good_signal = if_else(avg_30 >= avg_90, 1, 0)) %>%
        mutate(buy = if_else(good_signal - lag(good_signal) == 1, 1, 0),
               sell = if_else(good_signal - lag(good_signal) == -1, 1, 0)) %>%
        filter(Date >= "2020/06/01")

buys <- df %>%
        filter(buy == 1)

sell <- df %>%
        filter(sell == 1)

Writing the Plotly graph

i <- list(line = list(color = 'black'))
d <- list(line = list(color = 'red'))

fig <- df %>%
        plot_ly(x = ~Date, type = "candlestick",
                open = ~GME.Open, close = ~GME.Close,
                high = ~GME.High, low = ~GME.Low, name = "GME",
                increasing = i, decreasing = d, width = 800, height = 500) %>%
        add_lines(y = ~avg_30, name = "Closing 30D MvgAvg",
                  line = list(color = 'cadetblue')) %>%
        add_lines(y = ~avg_90, name = "Closing 90D MvgAvg",
                  line = list(color = 'blue')) %>%
        add_segments(x = buys$Date, xend = buys$Date, y = 0, yend = 500, line = list(color = "green"), name = "Buy Signal") %>%
        add_segments(x = sell$Date, xend = sell$Date, y = 0, yend = 500, line = list(color = "orange"), name = "Sell Signal") %>%
        layout(yaxis = list(title = "Price"))
        

The graph